code: def step(self, action):
      if action == 1:
          self.pos = self._get_new_pos(self.pos, self.wind_direction)
      self.num_steps += 1
      self.wind_direction = random.choice([0, 1, 2, 3])
      at_goal = self.pos == self.end_pos
      truncated = self.num_steps >= 200
      done = at_goal or truncated
      return (
          [[self.pos[0], self.pos[1]], self.wind_direction],
          100 * int(at_goal),
          done,
          truncated,
          {},
      )
  
file_path: ray/rllib/examples/env/windy_maze_env.py
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete, Tuple
  import logging
  import random
  from ray.rllib.env import MultiAgentEnv
label_desc: Use image node_id 3 for calling the WindyMazeEnv obj's underlying member method code with example usage: obj.step(action) and returns: list,  unknown,  done,  truncated,  dict